Rapid development of Cloud Computing and its increasing popularity in recent years has driven many commercial cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures and APIs and methods to access the cloud resources. Lack of standards has caused the collaboration and portability of cloud services a very complex task. In this paper we have identified the challenges involved in portability of cloud apps and analyzed the existing techniques for portability at platform level. In this paper, we propose an approach using Model Driven Engineering to develop SaaS applications in a cloud-agnostic way. We introduce DSkyL, an eclipse plugin for cloud application development using feature models and domain model analysis, which would support construction, customization, development and deployment of cloud application components across multiple clouds. It also reduces the application development time drastically. This paper aims to sketch the architecture of DSkyL and the major steps involved in the process.
Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API's and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.
Keyword:Feature Model Heterogenous cloud Model driven development Platform agnostic
<p class="Abstract">Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API’s and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.</p>
Data mining techniques are widely used for various educational researches. This article depicts the survey of various data mining techniques and tools which are used to guide students, course instructors, course developers, course administrators and organizations in respective fields based on future scope. This article also highlights how recommender systems rule the educational field though it's filtering mechanisms in recommending courses for students. It also illustrates future scope of data mining in educational needs.
Microarray allows us to efficiently analyse valuable gene expression data. In this paper we propose a effective methodology for analysis of microarrays. Earlier a new gridding algorithm is proposed to address all individual spots and to determine their borders. Then, a classical Gaussian Mixture Model (GMM) is used to analyse array spots more flexibly and adaptively. The Expectation Maximization (EM) algorithm is used to estimate GMM parameters by Maximum Likelihood (ML) approach. In this paper, we also addressing the problem of artifacts by detecting and compensate using GMM mixture components and artifacts data present in foreground and background spots are corrected by performing mathematical morphology and histogram analysis methods
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.